Paper: Large-scale Semantic Parsing via Schema Matching and Lexicon Extension

ACL ID P13-1042
Title Large-scale Semantic Parsing via Schema Matching and Lexicon Extension
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

Supervised training procedures for seman- tic parsers produce high-quality semantic parsers, but they have difficulty scaling to large databases because of the sheer number of logical constants for which they must see labeled training data. We present a technique for developing seman- tic parsers for large databases based on a reduction to standard supervised train- ing algorithms, schema matching, and pat- tern learning. Leveraging techniques from each of these areas, we develop a semantic parser for Freebase that is capable of pars- ing questions with an F1 that improves by 0.42 over a purely-supervised learning al- gorithm.